Abstract
Gender recognition has applications in human-computer interaction, biometric authentication, and targeted marketing. This paper presents an implementation of an algorithm for binary male/female gender recognition from face images based on a shunting inhibitory convolutional neural network, which has a reported accuracy on the FERET database of 97.2 %. The proposed hardware/software co-design approach using an ARM processor and FPGA can be used as an embedded system for a targeted marketing application to allow real-time processing. A threefold speedup is achieved in the presented approach compared to a software implementation on the ARM processor alone.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tivive, F.H.C., Bouzerdoum, A.: A gender recognition system using shunting inhibitory convolutional neural networks. In: International Joint Conference on Neural Networks, pp. 5336–5341. IEEE Press, New York (2006)
Tivive, F.H.C., Bouzerduom, A.: Efficient training algorithms for a class of shunting inhibitory convolutional neural networks. IEEE Trans. Neural Netw. 16(3), 541–556 (2005). IEEE Press, New York
Fregnac, Y., Monier, C., Chavane, F., Baudot, P., Graham, L.: Shunting inhibition, a silent step in visual computation. J. Physiol. 97, 441–451 (2003)
Wolin, L.: Gender issues in advertising—an oversight synthesis of research: 1970–2002. J. Advert. Res. 43, 111–129 (2003)
Ng, C.B., Tay, Y.H., Goi, B.M.: Recognizing human gender in computer vision: a survey. In: 12th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence, pp. 335–346 (2012)
Zheng, J., Lu, B.: A support vector machine classifier with automatic confidence. Neurocomputing 74(11), 1926–1935 (2011)
Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett. 4(33), 431–437 (2012)
Azarmehr, R., Laganiere, R., Lee, W.S., Xu, C., Laroche, D.: Real-time embedded age and gender classification in unconstrained video. In: Conference on Computer Vision and Pattern Recognition Workshops, pp. 56–64. IEEE Press, New York (2015)
Irick, K. M., DeBole, M., Narayanan V., Gayasen, A.: A hardware efficient support vector machine architecture for FPGA. In: International Symposium on Field-Programmable Custom Computing Machines, pp. 304–305. IEEE Press, New York (2008)
Irick, K., DeBole, M., Narayanan, V., Sharma, R., Moon, H., Mummareddy, S.: A unified streaming architecture for real time face detection and gender classification. In: International Conference on Field Programmable Logic and Applications, pp. 267–272. IEEE Press, New York (2007)
Ratnakar, A., More, G.: Real time gender recognition on FPGA. Int. J. Sci. Eng. Res. 6(2), 19–22 (2015)
Gudis, E., Lu, P., Berends, D., Kaighn, K., van der Wal, G., Buchanan, G., Chai S., Piacentino, M.: An embedded vision services framework for heterogeneous accelerators. In: Conference on Computer Vision and Pattern Recognition, pp. 598–603. IEEE Press, New York (2013)
Phillips, P.J., Moon, H., Rauss, P.J., Rizvi, S.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, A.TY., Biglari-Abhari, M., Wang, K.IK., Bouzerdoum, A., Tivive, F.H.C. (2016). Hardware/Software Co-design for a Gender Recognition Embedded System. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-42007-3_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42006-6
Online ISBN: 978-3-319-42007-3
eBook Packages: Computer ScienceComputer Science (R0)